The Point of Advanced Machine Learning: Understanding Cognitive Analytics

In contemporary business settings, Artificial Intelligence is largely synonymous with machine learning’s classic or advanced neural network capabilities. Several aspects of predictive analytics, timely recommendations, and sophisticated pattern recognition are attributed to these manifestations of machine learning, which many consider to be the summation of AI’s value to the enterprise today.

On the one hand, such a perception is certainly understandable. The present and future analytics capabilities of machine learning are more meritorious to most organizations than are conventional historic-facing Business Intelligence reports. The timeliness of the former is incomparable to the prolonged waiting characteristic of the latter, which seemingly supports this notion.

However, what has only recently emerged due to the proliferation of intelligent virtual assistants and their conversational capabilities is the mainstream adoption of an even more advanced form of analytics. Predicated on the advanced pattern recognition of machine learning and rooted in its foundation of process improvement (machine learning’s penchant for getting better at specific tasks over time), cognitive analytics effectively represents a third analytics era.

“We’re kind of evolving from that old standard kind of analytics to advanced ML/DL, to really now that cognitive piece,” acknowledged NTT Data Services CTO Kris Fitzgerald.

Part of the difficulty in understanding cognitive analytics is the montage of terms associated with cognitive computing, and the blurry distinctions between AI, machine learning, cognitive capabilities, and even certain elements of robotics. Machine learning forms the basis of cognitive analytics, while machine learning, cognitive computing, and robotics are all expressions of AI.

Moreover, cognitive analytics are particularly esteemed within the enterprise today because they play an integral role in elevating advanced machine learning to significantly improve what may very well be the most valued form of analytics: intelligent responses and predictions to real-time, conversational customer interactions.

“What’s happened is that I think again, this technology’s changing so fast, that now what you have is technology that is able to understand what you are saying as well as a human [can],” Fitzgerald revealed.

Episodic Memory
Perhaps more than any other characteristic, the point of departure between truly cognitive analytics and advanced machine learning analytics is the incorporation of episodic memory as a vital component of the interactions between intelligent virtual agents (such as Siri or Alexa) and humans. A number of digital personal assistants or Interactive Voice Response (IVR) systems have appeared to listen to callers and make dynamic responses based on speech or voice recognition capabilities. Cognitive analytics is actually able to do so largely because of its episodic memory capacity. Understanding what people are saying is merely, “One step towards getting the better answer; you then need to add episodic memory to it where it’s not just a FAQ list, but it learns to understand the conversation,” Fitzgerald said. Again, there is a marked difference between understanding the specific words someone is saying and applying them towards a granular understanding of an entire conversation or verbal exchange. Episodic memory is crucial in this regard because it enables digital assistants to comprehend what people mean with terms and phrases that are otherwise ambiguous.

Moreover, it is able to do so at discreet intervals (such as between exchanges with different customers) in a way which drastically enhances customer interactions. “If the first call was about a PC password issue, and the next call, for example, is about placing an order for a new MasterCard, it [the virtual agent] had to learn both those processes and that the word ‘that’ has a different context for each of those episodes,” Fitzgerald explained. In this example, episodic memory allows virtual agents to understand the fact that the word ‘that’ was used by a customer in the former example to reference the password issue, and in the latter example to reference dealing with the MasterCard order.

Machine Learning Underpinnings and Limits
Empowered by episodic memory and other AI capabilities, cognitive analytics facilitate greater understanding of the underlying meaning of conversational interactions within the specific context to which those analytics are applied. This aspect of those analytics is distinct from advanced machine learning capabilities—although machine learning does impact cognitive analytics. “Machine [learning] is just massive data finding a pattern,” Fitzgerald remarked. “That’s been the challenge today. People have done that. We’ve done some virtual agents with just that and they’re okay. But they don’t understand everything and I have to repeat myself [when using them].” However, by implementing cognitive capabilities such as episodic memory “during the conversation when you say what do you mean by ‘that’, the virtual agent has context,” Fitzgerald mentioned. Such context is necessary to understand equivocal terms, dangling modifiers, and the variations of meaning applied to human speech. “In machine learning, ‘that’ has no word,” Fitzgerald said. “But in the context of I’m doing a reservation, that is the reservation.”

Still, machine learning supports this higher cognitive capacity in a couple of ways. It consumes the initial sets of training data on which these analytics models are targeted. Also, it can be deployed to identify commonly found terms and phrases in which there are multiple meanings to discern how they might apply to certain situations. Fitzgerald recollected an instance in which an organization trained its virtual agents on “400 movies to begin to understand what those various terms are.” Nevertheless, the most pronounced difference between cognitive capabilities and even the best of machine learning is typified by episodic memory, in which a virtual agent is actually “listening to conversations, so it’s not that it has a better answer but it’s able to recognize the question better. It’s how we think; it’s how we correlate [ideas and language].”

Natural Language Processing
Natural Language Processing is another eminent AI technology which, when leveraged in tandem with deep learning and episodic memory, helps facilitate cognitive analytics. In regards to cognitive analytics, Fitzgerald observed “an element of that is NLP.” NLP provides a framework for intelligent agents to decouple meaning based on terms and their variations. It’s a viable means of adding to the contextualization of language to clarify semantics in the specific conversational exchanges that are vital to the usage of intelligent agents as real-time customer interfaces. Without it and facets of episodic memory, Fitzgerald commented that organizations would be forced to “think of every correlation of phrases you might say” to those agents to attain conversational understanding—an exhausting task. But with NLP and the upper echelons of cognitive analytics, the agent “can now listen and transpose and say okay, this is what I heard you say and now follow that pattern,” Fitzgerald maintained.

Automated Conversational Exchanges
Neural networks and advanced machine learning play a substantial role in, yet are not the zenith of, contemporary analytics. They buttress a broader range of cognitive capabilities which enable much better voice and speech recognition on the part of intelligent virtual agents. As such, they yield considerable hope for the automated customer service experiences that have become a shared, de facto expectation for AI today. Nonetheless, Fitzgerald discussed this hope with an unmistakable degree of realism: “We’re not there yet for complex stuff. I don’t mean complex by a complex word, but more of a complex thinking pattern. But, it’s learning fast.”

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